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Network dynamics has always been a meaningful topic deserving exploration inthe realm of academy. previous network models contain two parts: (1) generatingstructure as per user property; (2) changing property as per network structure.Properties in these models, however, cannot be interpreted to concept inprevalent social theories or empirical truth. Also, they usually treat everyonein an uniform fashion. While such assumption is quite misguiding, and thussaliently limits their performance. To overcome these flaws, we devise apersonalized evolving model for social network and opinion (PENO), wherecitizens' ideology is revealed by variable opinions and four dimensions ofpersonality are considered for each entity - leadership, openness,agreeableness, and neuroticism. Opinion propagates via social tie, tiegenerates from opinion affinity, and personalities integrally work with opinionand tie across evolution. To our best knowledge, PENO is the first attempt tointroduce personality impact in network dynamics and verify social science withreasonable visualization during simulation. We also present its probabilisticgraph and conceive iterative learning algorithm. Experiments show PENOoutperforms several state-of-the-art baselines over two typical predictiontasks - congress voting prediction for legislative bills and friendshipprediction on a book-commenting website. Finally, we discuss its scalability todo multi-task learning and transfer learning in daily scenarios.

Modeling Personalized Dynamics of Social Network and Opinion at Individual Level